Visibility: public Uploaded 13-08-2021 by Sergey Redyuk sklearn==0.18 numpy>=1.6.1 scipy>=0.9 18 runs
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  • openml-python python scikit-learn sklearn sklearn_0.18
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C-Support Vector Classification. The implementation is based on libsvm. The fit time complexity is more than quadratic with the number of samples which makes it hard to scale to dataset with more than a couple of 10000 samples. The multiclass support is handled according to a one-vs-one scheme. For details on the precise mathematical formulation of the provided kernel functions and how `gamma`, `coef0` and `degree` affect each other, see the corresponding section in the narrative documentation: :ref:`svm_kernels`.


CPenalty parameter C of the error termdefault: 1.0
cache_sizeSpecify the size of the kernel cache (in MB) class_weight : {dict, 'balanced'}, optional Set the parameter C of class i to class_weight[i]*C for SVC. If not given, all classes are supposed to have weight one The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))``default: 200
class_weightdefault: null
coef0Independent term in kernel function It is only significant in 'poly' and 'sigmoid'default: 0.0
decision_function_shapeWhether to return a one-vs-rest ('ovr') decision function of shape (n_samples, n_classes) as all other classifiers, or the original one-vs-one ('ovo') decision function of libsvm which has shape (n_samples, n_classes * (n_classes - 1) / 2) The default of None will currently behave as 'ovo' for backward compatibility and raise a deprecation warning, but will change 'ovr' in 0.19 .. versionadded:: 0.17 *decision_function_shape='ovr'* is recommended .. versionchanged:: 0.17 Deprecated *decision_function_shape='ovo' and None*default: null
degreeDegree of the polynomial kernel function ('poly') Ignored by all other kernelsdefault: 3
gammaKernel coefficient for 'rbf', 'poly' and 'sigmoid' If gamma is 'auto' then 1/n_features will be used insteaddefault: "auto"
kernelSpecifies the kernel type to be used in the algorithm It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or a callable If none is given, 'rbf' will be used. If a callable is given it is used to pre-compute the kernel matrix from data matrices; that matrix should be an array of shape ``(n_samples, n_samples)``default: "rbf"
max_iterHard limit on iterations within solver, or -1 for no limitdefault: -1
probabilityWhether to enable probability estimates. This must be enabled prior to calling `fit`, and will slow down that methoddefault: false
random_stateThe seed of the pseudo random number generator to use when shuffling the data for probability estimation.default: null
shrinkingWhether to use the shrinking heuristicdefault: true
tolTolerance for stopping criteriondefault: 0.001
verboseEnable verbose output. Note that this setting takes advantage of a per-process runtime setting in libsvm that, if enabled, may not work properly in a multithreaded contextdefault: false


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